CS 651 Machine Learning Jan 2018

Preamble

Machine learning is about designing programs that can learn without being explicitly programmed. It is a branch of Artificial Intelligence in which we learn concepts/ patterns/ hypotheses from Data by using heuristic based algorithms. Accordingly, this field is about study and implementation of two main category of algorithms: Supervised and Unsupervised. Supervised learning algorithms make use of data with known classification, aka labeled examples whereas Unsupervised learning algorithms use data with unknown classification, aka unlabeled examples. This field has become so popular that one can find machine leaning applications in virtually all domains ranging from identifying emails as spam or legitimate to automated vehicle guided system to game playing to credit card fraud detection. As this form is unlikely to become exact science, a learning method/algorithm needs to be evaluated and estimated for its performance on unseen data or the population.

Course Contents [Project Page]

Learning Problem, Designing a Learning System, Supervised Learning - Linear and Logistic regression, Decision Tree Learning, Instance-Based Learning, kNN and CBR, Bayesian Learning, Naive Bayes Classifier, Artificial Neural Network (ANN), Unsupervised Learning- K-Means Clustering, Association Rule Mining, Formulating and Evaluating Hypotheses, Computational Learning Theory, Issues and practical advice in Machine Learning,

Ref Book (TB): Tom Mitchell. Machine Learning, Mc Graw Hill, 1997.

References

Course Slides and other Reading material [A companion ML programming resources page]

Additional (Suggested) Readings

1. A Few Useful Things to Know about Machine Learning, Communication of the ACM, 2012 (another version)

2. Machine Learning & Artificial Intelligence: Main Developments in 2016 and Key Trends in 2017 @KDnuggets

3. Machine Learning and Cyber Security Resources @KDnuggets

Project Page

References for Project Ideas/ Datasets for Machine Learning Projects/Competitions

1. UCI Machine learning repository - Perhaps the biggest repository for ML applications/projects

2. Competitions@Kaggle and Datasets@Kaggle

3. Sample ML Projects - A big list can be found (Student ML projects at Stanford) - 2016, 2015, 2014 2013, 2012, 2011, 2010, 2009, 2008, 2007, 2006, 2005, 2004

4.Carlos Guestrin's class at CMU.

5. Goeff Gordon's class at CMU

6. Ray Mooney's class, UT Austin

7. Andreas Krause's class, Caltech

8. Data Science Student Projects at Radboud University.

Practical Tips and Advice

1. Increasing number of features results in ??

2.

Programming Environment and Tools

1. Machine Learning in Python

2. MATLAB

3. R Programming

4. Octav

5. Machine learning in Java

6. Weka

7. Google cloud machine leaning services

Evaluation Criteria

Mid Sem 20%

Quizzes 20%

End Sem 40%

Course Project 20% + 10% (Extra)

Some useful links

1. Machine Learning Course at Stanford (also at Coursera)

2. Machine Learning Course at Caltech

3. A Course in Machine Learning (CIML)

4. Open Class Room at Stanford on Machine Learning (Andrew Ng)

5. Machine Learning Group at Saarland University

6. Notes on various topics in Machine Learning

7. Machine Learning Resources list

Latest Happenings

1. @KDnuggets for recent trends/articles in Machine Learning/Data Science

2.